{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating new images with DCGAN\n",
    "\n",
    "In this example, we'll use TensorFlow 2.0 / Keras to implement DCGAN for generating new MNIST images.\n",
    "\n",
    "_The code is based on_ [https://github.com/eriklindernoren/Keras-GAN](https://github.com/eriklindernoren/Keras-GAN)<br />\n",
    "_Author of the base implemenation: Erik Linder-Norén_ <br />\n",
    "_License: MIT_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with the imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.layers import \\\n",
    "    Conv2D, Conv2DTranspose, BatchNormalization, Dropout, Input, Dense, Reshape, Flatten\n",
    "from tensorflow.keras.layers import LeakyReLU\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll continue with the definition of the generator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_generator(latent_input: Input):\n",
    "    \"\"\"\n",
    "    :param latent_input: the latent input\n",
    "    \"\"\"\n",
    "\n",
    "    model = Sequential([\n",
    "        # start with a fully connected layer to upsample the 1d latent vector\n",
    "        # the input_shape is the same as latent_input (excluding the mini-batch)\n",
    "        Dense(7 * 7 * 256, use_bias=False, input_shape=latent_input.shape[1:]),\n",
    "        BatchNormalization(), LeakyReLU(),\n",
    "\n",
    "        # reshape the noise to feed to the transposed convolutions\n",
    "        Reshape((7, 7, 256)),\n",
    "\n",
    "        # expand the input with transposed convolutions\n",
    "        Conv2DTranspose(filters=128, kernel_size=(5, 5), strides=(1, 1),\n",
    "                        padding='same', use_bias=False),\n",
    "        BatchNormalization(), LeakyReLU(),\n",
    "\n",
    "        # gradually reduce the volume depth\n",
    "        Conv2DTranspose(filters=64, kernel_size=(5, 5), strides=(2, 2),\n",
    "                        padding='same', use_bias=False),\n",
    "        BatchNormalization(), LeakyReLU(),\n",
    "\n",
    "        # final transposed convolution with tanh activation\n",
    "        # the generated image has only one channel\n",
    "        Conv2DTranspose(filters=1, kernel_size=(5, 5), strides=(2, 2),\n",
    "                        padding='same', use_bias=False, activation='tanh'),\n",
    "    ])\n",
    "\n",
    "    model.summary()\n",
    "\n",
    "    # this is forward phase\n",
    "    generated = model(latent_input)\n",
    "\n",
    "    # build model from the input and output\n",
    "    return Model(z, model(latent_input))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll define the disciriminator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_discriminator():\n",
    "    model = Sequential([\n",
    "        Conv2D(filters=64, kernel_size=(5, 5), strides=(2, 2),\n",
    "               padding='same', input_shape=(28, 28, 1)),\n",
    "        LeakyReLU(), Dropout(0.3),\n",
    "        Conv2D(filters=128, kernel_size=(5, 5), strides=(2, 2),\n",
    "               padding='same'),\n",
    "        LeakyReLU(), Dropout(0.3),\n",
    "        Flatten(),\n",
    "        Dense(1, activation='sigmoid'),\n",
    "    ])\n",
    "\n",
    "    model.summary()\n",
    "\n",
    "    image = Input(shape=(28, 28, 1))\n",
    "    output = model(image)\n",
    "\n",
    "    return Model(image, output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's continue with the training process. It alternates between training the discriminator with sets of real and fake images, and training the generator (using the combined discriminator/generator model):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(generator, discriminator, combined, steps, batch_size):\n",
    "    \"\"\"\n",
    "    Train the GAN system\n",
    "    :param generator: generator\n",
    "    :param discriminator: discriminator\n",
    "    :param combined: stacked generator and discriminator\n",
    "    we'll use the combined network when we train the generator\n",
    "    :param steps: number of alternating steps for training\n",
    "    :param batch_size: size of the minibatch\n",
    "    \"\"\"\n",
    "\n",
    "    # Load the dataset\n",
    "    (x_train, _), _ = mnist.load_data()\n",
    "\n",
    "    # Rescale in [-1, 1] interval\n",
    "    x_train = (x_train.astype(np.float32) - 127.5) / 127.5\n",
    "    x_train = np.expand_dims(x_train, axis=-1)\n",
    "\n",
    "    # Discriminator ground truths\n",
    "    real, fake = np.ones((batch_size, 1)), np.zeros((batch_size, 1))\n",
    "\n",
    "    latent_dim = generator.input_shape[1]\n",
    "\n",
    "    for step in range(steps):\n",
    "        # Train the discriminator\n",
    "\n",
    "        # Select a random batch of images\n",
    "        real_images = x_train[np.random.randint(0, x_train.shape[0], batch_size)]\n",
    "\n",
    "        # Random batch of noise\n",
    "        noise = np.random.normal(0, 1, (batch_size, latent_dim))\n",
    "\n",
    "        # Generate a batch of new images\n",
    "        generated_images = generator.predict(noise)\n",
    "\n",
    "        # Train the discriminator\n",
    "        discriminator_real_loss = discriminator.train_on_batch(real_images, real)\n",
    "        discriminator_fake_loss = discriminator.train_on_batch(generated_images, fake)\n",
    "        discriminator_loss = 0.5 * np.add(discriminator_real_loss, discriminator_fake_loss)\n",
    "\n",
    "        # Train the generator\n",
    "        # random latent vector z\n",
    "        noise = np.random.normal(0, 1, (batch_size, latent_dim))\n",
    "\n",
    "        # Train the generator\n",
    "        # Note that we use the \"valid\" labels for the generated images\n",
    "        # That's because we try to maximize the discriminator loss\n",
    "        generator_loss = combined.train_on_batch(noise, real)\n",
    "\n",
    "        # Display progress\n",
    "        if step % 1000 == 0:\n",
    "            print(\"%d [Discriminator loss: %.4f%%, acc.: %.2f%%] [Generator loss: %.4f%%]\" %\n",
    "                  (step, discriminator_loss[0], 100 * discriminator_loss[1], generator_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll define the `plot_generated_images` function, which generates a grid of newly generated MNIST images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_generated_images(generator):\n",
    "    \"\"\"\n",
    "    Display a nxn 2D manifold of digits\n",
    "    :param generator: the generator\n",
    "    \"\"\"\n",
    "    n = 10\n",
    "    digit_size = 28\n",
    "\n",
    "    # big array containing all images\n",
    "    figure = np.zeros((digit_size * n, digit_size * n))\n",
    "\n",
    "    latent_dim = generator.input_shape[1]\n",
    "\n",
    "    # n*n random latent distributions\n",
    "    noise = np.random.normal(0, 1, (n * n, latent_dim))\n",
    "\n",
    "    # generate the images\n",
    "    generated_images = generator.predict(noise)\n",
    "\n",
    "    # fill the big array with images\n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            slice_i = slice(i * digit_size, (i + 1) * digit_size)\n",
    "            slice_j = slice(j * digit_size, (j + 1) * digit_size)\n",
    "            figure[slice_i, slice_j] = np.reshape(generated_images[i * n + j], (28, 28))\n",
    "\n",
    "    # plot the results\n",
    "    plt.figure(figsize=(12, 10))\n",
    "    plt.axis('off')\n",
    "    plt.imshow(figure, cmap='Greys_r')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's put it all together. We'll start by instantiating the `generator`, the `discrimantor`, the `combined` model, and the training framework. Then, we'll initiate the training for 50,000 episodes. Finally, we'll plot a few images to see the results of the training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 12544)             802816    \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 12544)             50176     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)      (None, 12544)             0         \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 7, 7, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose (Conv2DTran (None, 7, 7, 128)         819200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 7, 7, 128)         512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 14, 14, 64)        204800    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 14, 14, 64)        256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 28, 28, 1)         1600      \n",
      "=================================================================\n",
      "Total params: 1,879,360\n",
      "Trainable params: 1,853,888\n",
      "Non-trainable params: 25,472\n",
      "_________________________________________________________________\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 14, 14, 64)        1664      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 7, 7, 128)         204928    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 6273      \n",
      "=================================================================\n",
      "Total params: 212,865\n",
      "Trainable params: 212,865\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "0 [Discriminator loss: 0.7216%, acc.: 13.50%] [Generator loss: 0.6998%]\n",
      "1000 [Discriminator loss: 0.6466%, acc.: 63.50%] [Generator loss: 0.6178%]\n",
      "2000 [Discriminator loss: 0.6953%, acc.: 54.50%] [Generator loss: 0.7598%]\n",
      "3000 [Discriminator loss: 0.6623%, acc.: 60.50%] [Generator loss: 0.7966%]\n",
      "4000 [Discriminator loss: 0.6823%, acc.: 58.00%] [Generator loss: 0.7377%]\n",
      "5000 [Discriminator loss: 0.6866%, acc.: 58.50%] [Generator loss: 0.7393%]\n",
      "6000 [Discriminator loss: 0.6615%, acc.: 64.50%] [Generator loss: 0.7711%]\n",
      "7000 [Discriminator loss: 0.6611%, acc.: 61.00%] [Generator loss: 0.7738%]\n",
      "8000 [Discriminator loss: 0.6925%, acc.: 54.50%] [Generator loss: 0.7533%]\n",
      "9000 [Discriminator loss: 0.7047%, acc.: 52.50%] [Generator loss: 0.7565%]\n",
      "10000 [Discriminator loss: 0.6797%, acc.: 55.50%] [Generator loss: 0.7483%]\n",
      "11000 [Discriminator loss: 0.6781%, acc.: 55.50%] [Generator loss: 0.7708%]\n",
      "12000 [Discriminator loss: 0.7004%, acc.: 53.50%] [Generator loss: 0.7711%]\n",
      "13000 [Discriminator loss: 0.6875%, acc.: 58.50%] [Generator loss: 0.7644%]\n",
      "14000 [Discriminator loss: 0.6763%, acc.: 62.00%] [Generator loss: 0.7766%]\n",
      "15000 [Discriminator loss: 0.6980%, acc.: 53.50%] [Generator loss: 0.7926%]\n",
      "16000 [Discriminator loss: 0.6656%, acc.: 62.00%] [Generator loss: 0.7630%]\n",
      "17000 [Discriminator loss: 0.6599%, acc.: 59.00%] [Generator loss: 0.7984%]\n",
      "18000 [Discriminator loss: 0.6609%, acc.: 61.00%] [Generator loss: 0.7780%]\n",
      "19000 [Discriminator loss: 0.6791%, acc.: 55.50%] [Generator loss: 0.7629%]\n",
      "20000 [Discriminator loss: 0.6699%, acc.: 58.50%] [Generator loss: 0.7730%]\n",
      "21000 [Discriminator loss: 0.7043%, acc.: 45.50%] [Generator loss: 0.7473%]\n",
      "22000 [Discriminator loss: 0.6737%, acc.: 56.50%] [Generator loss: 0.7763%]\n",
      "23000 [Discriminator loss: 0.6676%, acc.: 59.00%] [Generator loss: 0.8086%]\n",
      "24000 [Discriminator loss: 0.6750%, acc.: 56.00%] [Generator loss: 0.7243%]\n",
      "25000 [Discriminator loss: 0.6632%, acc.: 61.50%] [Generator loss: 0.7433%]\n",
      "26000 [Discriminator loss: 0.6861%, acc.: 52.50%] [Generator loss: 0.7909%]\n",
      "27000 [Discriminator loss: 0.6503%, acc.: 60.00%] [Generator loss: 0.8267%]\n",
      "28000 [Discriminator loss: 0.6994%, acc.: 51.50%] [Generator loss: 0.7598%]\n",
      "29000 [Discriminator loss: 0.6965%, acc.: 54.50%] [Generator loss: 0.8165%]\n",
      "30000 [Discriminator loss: 0.6786%, acc.: 58.50%] [Generator loss: 0.7686%]\n",
      "31000 [Discriminator loss: 0.6878%, acc.: 56.50%] [Generator loss: 0.7833%]\n",
      "32000 [Discriminator loss: 0.6820%, acc.: 55.00%] [Generator loss: 0.7456%]\n",
      "33000 [Discriminator loss: 0.6868%, acc.: 56.50%] [Generator loss: 0.7342%]\n",
      "34000 [Discriminator loss: 0.6734%, acc.: 55.00%] [Generator loss: 0.7791%]\n",
      "35000 [Discriminator loss: 0.7007%, acc.: 54.50%] [Generator loss: 0.7599%]\n",
      "36000 [Discriminator loss: 0.6763%, acc.: 59.00%] [Generator loss: 0.7411%]\n",
      "37000 [Discriminator loss: 0.6924%, acc.: 55.00%] [Generator loss: 0.7931%]\n",
      "38000 [Discriminator loss: 0.6713%, acc.: 60.00%] [Generator loss: 0.7928%]\n",
      "39000 [Discriminator loss: 0.7011%, acc.: 53.50%] [Generator loss: 0.8058%]\n",
      "40000 [Discriminator loss: 0.6813%, acc.: 54.50%] [Generator loss: 0.7315%]\n",
      "41000 [Discriminator loss: 0.6885%, acc.: 53.50%] [Generator loss: 0.7703%]\n",
      "42000 [Discriminator loss: 0.7018%, acc.: 51.00%] [Generator loss: 0.7477%]\n",
      "43000 [Discriminator loss: 0.6795%, acc.: 58.00%] [Generator loss: 0.7582%]\n",
      "44000 [Discriminator loss: 0.6749%, acc.: 60.00%] [Generator loss: 0.7479%]\n",
      "45000 [Discriminator loss: 0.6645%, acc.: 59.50%] [Generator loss: 0.7375%]\n",
      "46000 [Discriminator loss: 0.6817%, acc.: 54.50%] [Generator loss: 0.7297%]\n",
      "47000 [Discriminator loss: 0.6924%, acc.: 56.00%] [Generator loss: 0.7534%]\n",
      "48000 [Discriminator loss: 0.6573%, acc.: 65.50%] [Generator loss: 0.7660%]\n",
      "49000 [Discriminator loss: 0.6846%, acc.: 56.50%] [Generator loss: 0.7398%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "latent_dim = 64\n",
    "\n",
    "# Build the generator\n",
    "# Generator input z\n",
    "z = Input(shape=(latent_dim,))\n",
    "\n",
    "generator = build_generator(z)\n",
    "\n",
    "generated_image = generator(z)\n",
    "\n",
    "# we'll use Adam optimizer\n",
    "optimizer = Adam(0.0002, 0.5)\n",
    "\n",
    "# Build and compile the discriminator\n",
    "discriminator = build_discriminator()\n",
    "discriminator.compile(loss='binary_crossentropy',\n",
    "                      optimizer=optimizer,\n",
    "                      metrics=['accuracy'])\n",
    "\n",
    "# Only train the generator for the combined model\n",
    "discriminator.trainable = False\n",
    "\n",
    "# The discriminator takes generated image as input and determines validity\n",
    "real_or_fake = discriminator(generated_image)\n",
    "\n",
    "# Stack the generator and discriminator in a combined model\n",
    "# Trains the generator to deceive the discriminator\n",
    "combined = Model(z, real_or_fake)\n",
    "combined.compile(loss='binary_crossentropy', optimizer=optimizer)\n",
    "\n",
    "# train the GAN system\n",
    "train(generator, discriminator, combined, steps=50000, batch_size=100)\n",
    "\n",
    "# display some random generated images\n",
    "plot_generated_images(generator)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
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